new line of sight distribution handling for multiple distributions an…

…d separate list of dictionary, including generalized extreme value distribution

hierarc/Diagnostics/goodness_of_fit.py

@@ -28,6 +28,7 @@ def plot_ddt_fit(
         cosmo,
         kwargs_lens,
         kwargs_kin,
+        kwargs_los,
         color_measurement=None,
         color_prediction=None,
         redshift_trend=False,
@@ -64,7 +65,7 @@ def plot_ddt_fit(
                     ddt_model_sigma,
                     dd_model_mean,
                     dd_model_sigma,
-                ) = likelihood.ddt_dd_model_prediction(cosmo, kwargs_lens=kwargs_lens)
+                ) = likelihood.ddt_dd_model_prediction(cosmo, kwargs_lens=kwargs_lens, kwargs_los=kwargs_los)
 
                 ddt_name_list.append(name)
                 ddt_model_mean_list.append(ddt_model_mean)
@@ -134,13 +135,14 @@ def plot_ddt_fit(
         ax.legend()
         return f, ax
 
-    def kin_fit(self, cosmo, kwargs_lens, kwargs_kin):
+    def kin_fit(self, cosmo, kwargs_lens, kwargs_kin, kwargs_los):
         """Plots the prediction and the uncorrelated error bars on the individual lenses
         currently works for likelihood classes 'TDKinGaussian', 'KinGaussian'.
 
         :param cosmo: astropy.cosmology instance
         :param kwargs_lens: lens model parameter keyword arguments
         :param kwargs_kin: kinematics model keyword arguments
+        :param kwargs_los: line of sight list of dictionaries
         :return: list of name, measurement, measurement errors, model prediction, model
             prediction error
         """
@@ -160,7 +162,7 @@ def kin_fit(self, cosmo, kwargs_lens, kwargs_kin):
                 sigma_v_predict_mean,
                 cov_error_predict,
             ) = likelihood.sigma_v_measured_vs_predict(
-                cosmo, kwargs_lens=kwargs_lens, kwargs_kin=kwargs_kin
+                cosmo, kwargs_lens=kwargs_lens, kwargs_kin=kwargs_kin, kwargs_los=kwargs_los
             )
 
             if sigma_v_measurement is not None:
@@ -188,6 +190,7 @@ def plot_kin_fit(
         cosmo,
         kwargs_lens,
         kwargs_kin,
+        kwargs_los,
         color_measurement=None,
         color_prediction=None,
     ):
@@ -197,19 +200,20 @@ def plot_kin_fit(
         :param cosmo: astropy.cosmology instance
         :param kwargs_lens: lens model parameter keyword arguments
         :param kwargs_kin: kinematics model keyword arguments
+        :param kwargs_los: line of sight list of dictionaries
         :param color_measurement: color of measurement
         :param color_prediction: color of model prediction
         :return: fig, axes of matplotlib instance
         """
-        logL = self._sample_likelihood.log_likelihood(cosmo, kwargs_lens, kwargs_kin)
+        logL = self._sample_likelihood.log_likelihood(cosmo, kwargs_lens, kwargs_kin, kwargs_los=kwargs_los)
         print(logL, "log likelihood")
         (
             sigma_v_name_list,
             sigma_v_measurement_list,
             sigma_v_measurement_error_list,
             sigma_v_model_list,
             sigma_v_model_error_list,
-        ) = self.kin_fit(cosmo, kwargs_lens, kwargs_kin)
+        ) = self.kin_fit(cosmo, kwargs_lens, kwargs_kin, kwargs_los)
 
         f, ax = plt.subplots(1, 1, figsize=(int(len(sigma_v_name_list) / 2), 4))
         ax.errorbar(
@@ -256,6 +260,7 @@ def plot_ifu_fit(
         cosmo,
         kwargs_lens,
         kwargs_kin,
+        kwargs_los,
         lens_index,
         bin_edges,
         show_legend=True,
@@ -268,6 +273,7 @@ def plot_ifu_fit(
         :param cosmo: astropy.cosmology instance
         :param kwargs_lens: lens model parameter keyword arguments
         :param kwargs_kin: kinematics model keyword arguments
+        :param kwargs_los: line of sight list of dictionaries
         :param lens_index: int, index in kwargs_lens to be plotted (needs to be of type
             'IFUKinCov')
         :param bin_edges: radial bin edges in arc seconds. If number, then uniform
@@ -293,7 +299,7 @@ def plot_ifu_fit(
             sigma_v_predict_mean,
             cov_error_predict,
         ) = likelihood.sigma_v_measured_vs_predict(
-            cosmo, kwargs_lens=kwargs_lens, kwargs_kin=kwargs_kin
+            cosmo, kwargs_lens=kwargs_lens, kwargs_kin=kwargs_kin, kwargs_los=kwargs_los
         )
 
         if len(np.atleast_1d(bin_edges)) < 2:

hierarc/Likelihood/KDELikelihood/kde_likelihood.py

@@ -64,9 +64,9 @@ def init_loglikelihood(self):
             )
             self.loglikelihood = self.kdelikelihood()
         else:
-            raise ValueError(
+            raise NameError(
                 "likelihood_type %s not supported! Supported are %s."
-                % (likelihood_type, LIKELIHOOD_TYPES)
+                % (self.loglikelihood_type, LIKELIHOOD_TYPES)
             )
 
     def kdelikelihood(self):

hierarc/Likelihood/LensLikelihood/double_source_plane.py

@@ -30,14 +30,15 @@ def __init__(
         self._normalized = normalized
 
     def lens_log_likelihood(
-        self, cosmo, kwargs_lens=None, kwargs_kin=None, kwargs_source=None
+        self, cosmo, kwargs_lens=None, kwargs_kin=None, kwargs_source=None, kwargs_los=None
     ):
         """Log likelihood of the data given a model.
 
         :param cosmo: astropy.cosmology instance
         :param kwargs_lens: keyword arguments of lens
         :param kwargs_kin: keyword arguments of kinematics
         :param kwargs_source: keyword argument of source
+        :param kwargs_los: keyword argument list of line of sight
         :return: log likelihood of data given model
         """
         beta_model = self._beta_model(cosmo)

hierarc/Likelihood/cosmo_likelihood.py

@@ -27,8 +27,8 @@ def __init__(
         gamma_in_distribution="NONE",
         log_m2l_sampling=False,
         log_m2l_distribution="NONE",
-        kappa_ext_sampling=False,
-        kappa_ext_distribution="NONE",
+        los_sampling=False,
+        los_distributions=None,
         alpha_lambda_sampling=False,
         beta_lambda_sampling=False,
         alpha_gamma_in_sampling=False,
@@ -73,8 +73,10 @@ def __init__(
          according to the lens posterior kwargs 'lambda_scaling_property'
         :param beta_lambda_sampling: bool, if True samples a parameter beta_lambda, which scales lambda_mst linearly
          according to the lens posterior kwargs 'lambda_scaling_property_beta'
-        :param kappa_ext_sampling: bool, if True samples a global external convergence parameter
-        :param kappa_ext_distribution: string, distribution function of the kappa_ext parameter
+        :param los_sampling: if sampling of the parameters should be done
+        :type los_sampling: bool
+        :param los_distributions: what distribution to be sampled
+        :type los_distributions: list of str
         :param anisotropy_sampling: bool, if True adds a global stellar anisotropy parameter that alters the single lens
         kinematic prediction
         :param anisotropy_model: string, specifies the stellar anisotropy model
@@ -106,7 +108,8 @@ def __init__(
         if sigma_v_systematics is True:
             normalized = True
         self._likelihoodLensSample = LensSampleLikelihood(
-            kwargs_likelihood_list, normalized=normalized
+            kwargs_likelihood_list, normalized=normalized,
+            los_distributions=los_distributions
         )
         self.param = ParamManager(
             cosmology,
@@ -127,8 +130,8 @@ def __init__(
             sne_distribution=sne_distribution,
             z_apparent_m_anchor=z_apparent_m_anchor,
             sigma_v_systematics=sigma_v_systematics,
-            kappa_ext_sampling=kappa_ext_sampling,
-            kappa_ext_distribution=kappa_ext_distribution,
+            los_sampling=los_sampling,
+            los_distributions=los_distributions,
             anisotropy_sampling=anisotropy_sampling,
             anisotropy_model=anisotropy_model,
             anisotropy_distribution=anisotropy_distribution,
@@ -188,7 +191,7 @@ def likelihood(self, args, kwargs_cosmo_interp=None):
             if args[i] < self._lower_limit[i] or args[i] > self._upper_limit[i]:
                 return -np.inf
 
-        kwargs_cosmo, kwargs_lens, kwargs_kin, kwargs_source = self.param.args2kwargs(
+        kwargs_cosmo, kwargs_lens, kwargs_kin, kwargs_source, kwargs_los = self.param.args2kwargs(
             args
         )
         if self._cosmology == "oLCDM":
@@ -215,6 +218,7 @@ def likelihood(self, args, kwargs_cosmo_interp=None):
             kwargs_lens=kwargs_lens,
             kwargs_kin=kwargs_kin,
             kwargs_source=kwargs_source,
+            kwargs_los=kwargs_los,
         )
 
         if self._sne_evaluate is True: